@inproceedings{FHPBRDDJ03,
  author = {Richard J.~Freuler and Michael J.~Hoffmann and Theodore P.~Pavlic and James M.~Beams and Jeffrey P.~Radigan and Prabal K.~Dutta and John T.~Demel and Erik D.~Justen},
  title = {Experiences with a Comprehensive Freshman Hands-on Course--Designing, Building, and Testing Small Autonomous Robots},
  booktitle = {Proceedings of the 2003 American Society for Engineering Education Annual Conference \& Exposition},
  year = {2003},
  abstract = {During the past ten years, The Ohio State University's College of Engineering has been aggressively addressing the issue of student retention. A major element in this effort is the development of a first-year engineering program that has moved from a series of related but separate courses for first-year engineering fundamentals to a framework that involves two course sequences with tightly coupled courses. Engineering orientation, engineering graphics, and engineering problem solving with computer programming are now offered in each of two course sequences, one called the Fundamentals of Engineering and the other the Fundamentals of Engineering for Honors. These course sequences retain part of the traditional material but now projects. Teamwork, project roles in both with a design/build project course in the Fundamentals of Engineering for Honors sequence that serves as a academic year.}
}

@mastersthesis{Pavlic07,
  author = {Theodore P.~Pavlic},
  title = {Optimal Foraging Theory Revisited},
  school = {The Ohio State University},
  year = {2007},
  address = {Columbus, OH},
  abstract = {Optimal foraging theory explains adaptation via natural selection through quantitative models. Behaviors that are most likely to be favored by natural selection can be predicted by maximizing functions representing Darwinian fitness. Optimization has natural applications in engineering, and so this approach can also be used to design behaviors of engineered agents. In this thesis, we generalize ideas from optimal foraging theory to allow for its easy application to engineering design. By extending standard models and suggesting new value functions of interest, we enhance the analytical efficacy of optimal foraging theory and suggest possible optimality reasons for previously unexplained behaviors observed in nature. Finally, we develop a procedure for maximizing a class of optimization functions relevant to our general model. As designing strategies to maximize returns in a stochastic environment is effectively an optimal portfolio problem, our methods are influenced by results from modern and post-modern portfolio theory. We suggest that optimal foraging theory could benefit by injecting updated concepts from these economic areas.},
  pages = {122},
  url = {http://www.ohiolink.edu/etd/view.cgi?acc_num=osu1181936683}
}

@article{PavlicPassino09,
  author = {Theodore P.~Pavlic and Kevin M.~Passino},
  title = {Foraging theory for autonomous vehicle speed choice},
  journal = {Engineering Applications of Artificial Intelligence},
  year = {2009},
  volume = {22},
  pages = {482--489},
  number = {3},
  month = {April},
  abstract = {We consider the optimal control design of an abstract autonomous vehicle (AAV). The AAV searches an area for tasks that are detected with a probability that depends on vehicle speed, and each detected task can be processed or ignored. Both searching and processing are costly, but processing also returns rewards that quantify designer preferences. We generalize results from the analysis of animal foraging behavior to model the AAV. Then, using a performance metric common in behavioral ecology, we explicitly find the optimal speed and task processing choice policy for a version of the AAV problem. Finally, in simulation, we show how parameter estimation can be used to determine the optimal controller online when density of task types is unknown.},
  doi = {10.1016/j.engappai.2008.10.017},
  issn = {0952-1976},
  keywords = {Intelligent control, optimal control, task-type choice, speed-accuracy trade-off, speed-cost trade-off, decision-making algorithms},
  url = {http://www.sciencedirect.com/science/article/B6V2M-4VBC5KR-2/2/fc603922585631f6d6e1b5efa76551ef}
}

@inproceedings{PavlicPassino09_ICAM2009_CTP_poster,
  author = {Theodore P.~Pavlic and Kevin M.~Passino},
  title = {Cooperative Task Processing},
  booktitle = {Proceedings of the {ICAM} 2009 Symposium: Emergence in Physical, Biological, and Social Systems {IV}},
  year = {2009},
  note = {Poster abstract},
  address = {Ann Arbor, Michigan},
  month = {November 13,}
}

@inproceedings{PavlicPassino10_SocBio_DACTP,
  author = {Theodore P.~Pavlic and Kevin M.~Passino},
  title = {Design and Analysis of Cooperative Task Processing Agents},
  booktitle = {Proceedings of the Third Annual Frontiers in Life Sciences conference~-- Social Biomimicry: Insect Societies and Human Design},
  year = {2010},
  address = {Tempe, Arizona},
  month = {February 18--20,},
  note = {Poster abstract}
}

@article{PavlicPassino10,
  author = {Theodore P.~Pavlic and Kevin M.~Passino},
  title = {When rate maximization is impulsive},
  journal = {Behavioral Ecology and Sociobiology},
  year = {2010},
  volume = {64},
  pages = {1255--1265},
  number = {8},
  month = {August},
  abstract = {Although optimal foraging theory predicts that natural selection should favor animal behaviors that maximize long-term rate of gain, behaviors observed in the laboratory tend to be impulsive. In binary-choice experiments, despite the long-term gain of each alternative, animals favor short handling times. Most explanations of this behavior suggest that there is hidden rationality in impulsiveness. Instead, we suggest that simultaneous and mutually exclusive binary-choice encounters are often unnatural and thus immune to the effects of natural selection. Using a simulation of an imperfect forager, we show how a simple strategy (i.e., an intuitive model of animal behavior) that maximizes long-term rate of gain under natural conditions appears to be impulsive under operant laboratory conditions. We then show how the accuracy of this model can be verified in the laboratory by biasing subjects with a short pre-experiment ad libitum high-quality feeding period. We also show a similar behavioral mechanism results in diet preferences that are qualitatively consistent with the digestive rate model of foraging (i.e., foraging under digestive rate constraints).},
  doi = {10.1007/s00265-010-0940-1},
  keywords = {impulsiveness; impulsivity; rationality; self-control; optimal foraging; simultaneous encounters}
}

@phdthesis{Pavlic10,
  author = {Theodore P.~Pavlic},
  title = {Design and Analysis of Optimal Task-Processing Agents},
  school = {The Ohio State University},
  year = {2010},
  address = {Columbus, OH},
  month = {August},
  pages = {198},
  url = {http://rave.ohiolink.edu/etdc/view.cgi?acc_num=osu1281462093}
}

@article{PavlicPassino11a,
  author = {Theodore P.~Pavlic and Kevin M.~Passino},
  title = {The Sunk-cost Effect as an Optimal Rate-maximizing Behavior},
  journal = {Acta Biotheoretica},
  year = {2011},
  volume = {59},
  pages = {53--66},
  number = {1},
  abstract = {Optimal foraging theory has been criticized for underestimating patch exploitation time. However, proper modeling of costs not only answers these criticisms, but it also explains apparently irrational behaviors like the sunk-cost effect. When a forager is sure to experience high initial costs repeatedly, the forager should devote more time to exploitation than searching in order to minimize the accumulation of said costs. Thus, increased recognition or reconnaissance costs lead to increased exploitation times in order to reduce the frequency of future costs, and this result can be used to explain paradoxical human preference for higher costs. In fact, this result also provides an explanation for how continuing a very costly task indefinitely provides the optimal long-term rate of gain; the entry cost of each new task is so great that the forager avoids ever returning to search. In general, apparently irrational decisions may be optimal when considering the lifetime of a forager within a larger system.},
  doi = {10.1007/s10441-010-9107-8},
  keywords = {solitary animal behavior; patch residence time; rationality; Concorde fallacy; escalation error; optimal foraging theory}
}

@article{PavlicPassino11b,
  author = {Theodore P.~Pavlic and Kevin M.~Passino},
  title = {Generalizing foraging theory for analysis and design},
  journal = {International Journal of Robotics Research [Special Issue on Stochasticity in Robotics and Bio-Systems Part~1]},
  year = {2011},
  volume = {30},
  pages = {505--523},
  number = {5},
  month = {April},
  abstract = {Foraging theory has been the inspiration for several decision-making algorithms for task-processing agents facing random environments. As nature selects for foraging behaviors that maximize lifetime calorie gain or minimize starvation probability, engineering designs are favored that maximize returned value (e.g. profit) or minimize the probability of not reaching performance targets. Prior foraging-inspired designs are direct applications of classical optimal foraging theory (OFT). Here, we describe a generalized optimization framework that encompasses the classical OFT model, a popular competitor, and several new models introduced here that are better suited for some task-processing applications in engineering. These new models merge features of rate maximization, efficiency maximization, and risk-sensitive foraging while not sacrificing the intuitive character of classical OFT. However, the central contributions of this paper are analytical and graphical methods for designing decision-making algorithms guaranteed to be optimal within the framework. Thus, we provide a general modeling framework for solitary agent behavior, several new and classic examples that apply to it, and generic methods for design and analysis of optimal task-processing behaviors that fit within the framework. Our results extend the key mathematical features of optimal foraging theory to a wide range of other optimization objectives in biological, anthropological, and technological contexts.},
  doi = {10.1177/0278364910396551},
  keywords = {agent-based models; biomimicry; decision making; Markov renewal processes; mathematical biology; optimization; solitary agent behavior}
}

@techreport{PavlicPassino11c,
  author = {Theodore P.~Pavlic and Kevin M.~Passino},
  title = {Cooperative Task-Processing Networks: Parallel Computation of Non-trivial Volunteering Equilibria},
  institution = {The Ohio State University},
  year = {2011},
  number = {OSU-CISRC-3/11-TR05},
  url = {ftp://ftp.cse.ohio-state.edu/pub/tech-report/2011/TR05.pdf}
}

@inproceedings{PavlicPassino11d,
  author = {Theodore P.~Pavlic and Kevin M.~Passino},
  title = {Cooperative Task-Processing Networks},
  year = {2011},
  abstract = {This paper introduces a novel framework for the analysis and design of distributed agents that must complete externally generated tasks but also can volunteer to process tasks encountered by other agents. A distributed asynchronous volunteering policy is presented that dynamically adjusts task flow around the network of agents. It is shown that even though agents independently adjust their tendency to volunteer to process tasks from other agents, the set of all volunteering tendencies converges to the unique Nash equilibrium of a cooperation game. An artificial cooperation trading economy ensures that at the equilibrium, non-zero cooperation tendencies are possible and vary across agents. In particular, an agent with relatively high task-encounter rate not only provides more incentive for connected neighbors to cooperate with it but also has less incentive to volunteer to cooperate with other agents. The framework is shown via simulation to be applicable to autonomous air vehicles, and the mathematical results of the paper are also shown to be consistent with classic studies of cooperation from science.},
  booktitle = {Proceedings of the Second International Workshop on Networks of Cooperating Objects, {CONET}~2011},
  address = {Chicago, IL, USA},
  month = {April 11,}
}

@techreport{PSWW11,
  author = {Theodore P.~Pavlic and Paolo A.~G.~Sivilotti and Alan D.~Weide and Bruce W.~Weide},
  title = {Comments on 'Adaptive Cruise Control: Hybrid, Distributed, and Now Formally Verified'},
  institution = {The Ohio State University},
  year = {2011},
  number = {OSU-CISRC-7/11-TR22},
  url = {ftp://ftp.cse.ohio-state.edu/pub/tech-report/2011/TR22.pdf}
}

@inproceedings{OKORSWP11_NSF_poster,
  author = {{\"{U}}mit {\"{O}}zg{\"{u}}ner and Ashok Krishnamurthy and F{\"{u}}sun {\"{O}}zg{\"{u}}ner and Keith Redmill and Paul Sivilotti and Bruce Weide and Ted Pavlic},
  title = {{CPS}: Autonomous Driving in Urban Environments},
  booktitle = {Proceedings of the 2011 {NSF} {CPS} {PI} Meeting},
  year = {2011},
  address = {National Harbor, MD},
  month = {August 1--2,},
  note = {Poster abstract}
}

@inproceedings{PSWW11_CSLETCM2011_VACC_poster,
  author = {Theodore P.~Pavlic and Paolo A.~G.~Sivilotti and Alan D.~Weide and Bruce W.~Weide},
  title = {Verification of Smooth and Close Collision-Free Cruise Control},
  year = {2011},
  note = {Poster abstract},
  year = {2011},
  address = {Urbana, Illinois},
  month = {October 20--21,},
  booktitle = {Proceedings of the 2011 Symposium on Control and Modeling: Cyber-Physical Systems}
}

@inproceedings{Pavlic11a,
  author = {Theodore P.~Pavlic},
  title = {Stigmergic Memory for Real-time Primal-space Distributed Optimization under Constraints},
  year = {2011},
  abstract = {In this paper, we propose a decentralized primal-space algorithm for constrained non-linear optimization that coordinates agent trajectories using stigmergic memory present in the system. There are several real-time distributed resource allocation applications amenable to these methods. Moreover, there may be other distributed algorithms built within cyberphysical systems that can leverage physical variables as stigmergic shared memory in lieu of direct communication.},
  booktitle = {Proceedings of the 50th {IEEE} Conference on Decision and Control and European Control Conference, {CDC}-{ECC}~2011},
  address = {Orlando, Florida, USA},
  month = {December 12--15,},
  note = {Submitted}
}